arXiv:2610.00623v1 Announce Type: new
Abstract: Speculative decoding has achieved substantial lossless speedups for LLMs, but remains less effective for large vision-language models (LVLMs), where li...
By Wenhan Yang, Anirudh Rao, Ashwin Chandra
The paper shows that long‑context drafting can be performed with short‑context drafters that are frozen and run beyond their training length, using two inference‑time mechanisms to keep their cost bounded. By bounding the attention cache and re‑basing rotary position indices, the authors demonstrate that the speedup achieved with a short‑context drafter persists up to two million tokens across multiple target‑drafter pairs and corpora. They prove that when a common index shift leaves the drafter’s computation unchanged, the acceptance gap can be tightly bounded, ensuring consistent performance.
By Aref Jafari, Vansh Bhatia, Matt Qin, Subhajit Dutta Chowdhury, Ben Lee, Vikram Appia, Ali Ghodsi, Mehdi Rezagholizadeh
arXiv:2608.30427v1 Announce Type: cross
Abstract: Speculative decoding speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, pres...
By Ephrem Wu
arXiv:2607. 21535v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation by having a cheap draft propose tokens that a target verifies in parallel.
By Alagappan Valliappan
arXiv:2608. 01207v2 Announce Type: replace-cross Abstract: Test-time scaling lifts large language model reasoning by sampling many candidate solutions and selecting among them, yet the same recipe transfers poorly to vision-language models (VLMs): recent work shows that simple majority voting beats selection methods built on the model's own self-verification, apparently because at the selection layer an image-grounded answer and a confident guess from the language prior look the same.
By Puzhuo Zheng, Hasan Kurban
arXiv:2609.26638v1 Announce Type: new
Abstract: Autoregressive OCR vision-language models accurately convert document images into text and structured markup, but require one sequential decoding step...
By Dohyun Kim, Sungjun Han, Hyungguk Kim, Yusik Kim, Jamin Shin, Paul Hongsuck Seo, Hongjoon Ahn